garry’s posthaven

Some of the most famous projects out there are sort of subject to this, right? Even AlphaGo, if you add another square or add another row or column, it actually breaks the model because there's not a deeper meaning--

It does and you can see this, too, if you read... OpenAI made a lot of noises from a publicity perspective about their "too dangerous to release" language model that would synthesize paragraphs of text. 

And when you read them, they were locally coherent, but globally incoherent because of this very phenomenon where they don't have a model of the world.

They're incredibly useful in complicated matrices, but there's not a deeper meaning.

What I would love to see is more effort being spent on systems that are human-like in this way, that actually learning that model of the world and learn high-level concepts and can reason so that we can build something that's closer to a human brain and less like the amazing Cambrian explosion of different very neural intelligences that are all trained to synthesize new songs or to play a new video game or to do image labeling or something. I think that that's why we started Vicarious and what I'd love for more companies to be working on, too.

The origins of AI kind of started this way, as well, didn't it? First text-to-speech models, for instance were trying to use neural networks. I guess link Vicarious to this sort of lineage of AI and how this stuff sort of started in this Cambrian era of the 60s and 70s.

So, I think the debate about how to build intelligence actually goes way further back than the current arguments happening or even the ones that happened in the early days of artificial intelligence in the 70s and 60s. 

You look back towards the philosophers and you see the thinkers like Plato or Kant who are arguing for a human mind that uses symbolic primitives or platonic solids or pure reason in order to understand the world and then there are others that are more in the lineage of people like Skinner who believe that everything is behavior and the human mind is effectively blank and there's nothing that's innate. I would argue that those two extremes are paradigms of thinking from a philosophical and then later from a psychological or psychophysics perspective moving into an artificial intelligence perspective, that pendulum swing of is everything learned or is everything hard-coded. Is it nature or is it nurture has been with us for a really long time and I think because a lot of the recent successes commercially have been with using very large power with very simple algorithms to create systems that are economically valuable or at least show flashy demos, that's kind of swung the pendulum in favor of viewing the mind or the artificial intelligent mind anyway as something that is all on the nurture column. 

If you take a really close look at the literature and the neuroscience and the cognitive science communities, there's so much that humans are able to do from incredibly young ages and there's no way to bridge the gap between our current artificial intelligence systems that take 14,000 years to learn how to manipulate just one Rubik's cube and a human child which can do it in two years. There's a very significant gap that I think needs to be bridged by bringing more innateness, more nature into the way we think about how to build artificial intelligence and that's been one of the north stars for Vicarious is thinking, okay, for the things that need to be learned, we should learn them. 

But for the things that are likely to be innate, or we can find strong biological evidence for being innate, then we should take advantage of that and use more structure in the models that we create. 

For the people who are more deep learning purists, I would observe that a lot of what's taken for granted in modern deep learning systems, things like local receptive fields or convolution, or reinforcement learning or batch normalization are all either biologically-inspired directly or after the fact, you could look back at the biology and realize hey, there's actually a really strong neural correlate for this. I think that provides some encouragement that we're looking in the right direction and seeing that what's gotten us to where we are now in our quest for intelligent machines is actually looking for more innate structures.

So, in terms of innate structures, we both have young children who are growing very quickly and going from basically Tamagotchi mode into real human mode, which is amazing to see. Are there parallels in types of learning systems that sort of layer on top of one another or is that a salient feature of the systems you're focused on and building?

Yeah, I think so. I think there's a hierarchy of skills that we form, a hierarchy of representations that we form as humans as we develop and the learning systems that we train in Vicarious inside the robots exhibit many of the same kinds of properties where by having access to one set of concepts, you can construct high-level concepts. And that was something that if it wasn't published in the Science Robotics paper we released last year it will be in a follow-up to that paper. 

I think drawing from our experience as humans and from the cognitive science and neuroscience communities is something that can be a really powerful accelerant for figuring out what are the right directions to point the next iterations of our AI architectures beyond well, let's just get a bigger computer.

How do you break it down into smaller pieces? Or is that not possible for something of this sort of magnitude? You know the classic standard SaaS view of software is ship something to a small number of users, make them very happy, and then iterate from there. And I don't even know how you apply that to something like AGI.

So, I think all of the big technologies or virtually all of the big technologies that have come to reshape society have been created using a really similar blueprint or recipe. 

Like I think about Amazon. To create an everything store where you can order anything and it gets there the next day, is a crazy idea, circa pre-internet or even circa early internet. And to build it, Jeff had to make a core technology and apply it to one very narrow thing, which he started with books. And so, inventing e-commerce and using a distribution center model for doing the shipments was enough to kind of get the flywheel spinning. And then the more you turn it, you go from books to books and CDs and to CDs and games and you expand outward and eventually you sell everything to anyone really fast. 

When I think about Elon, Elon started SpaceX to build a Mars colony. And you can't build a Mars colony in one go. And if you want a Mars colony, what you really want is a space logistics company. And so, he started with, the long-term goal for that was to get large, reusable rockets. And the short-term one was let's build small, disposable rockets as a stepping stone to get to the large, reusable ones which make the Mars colony possible. 

And you can kind of look at any of the very large, successful companies that have come to shape an industry or an aspect of society and they all follow the same footprint. So, for us as a robotics and AGI company, we wanted this any robot, any task, no programming, just language. 

We're going to get there one step at a time where we start off with a small number of different robots doing a small number different tasks and every time we add a new task, the robots get more valuable, we get more customers, we get more money which lets us make the robots do more tasks and so on. And we fly that wheel until the robots really can do anything including solve very challenging problems like the ones that face us in society today.

Robotics is sort of the way to have a very direct economic impact very quickly.

And I also say that I like businesses that while it's good to have a very long-term, like in 30 years, I want to cure all disease or something kind of ambition for society, but I especially love businesses who can create value all along the way. And so you don't have to wait 30 years to find out if your joke was funny, you can create value for everyone every time you have a small success it gets you closer to that long-term goal and it also helps people in the immediate term. And so, that's what we're trying to do at Vicarious.

There's so many problems in robotics that require basically a lot of if statements and one off work and a lot of calibration and have you been able to apply more general techniques to have a machine that teaches itself to learn?

I think we've made a lot of good progress so far on Vicarious' mission and our customers certainly appreciate it in the sense that most of the customers we talk to, they don't own any robots. They've never been able to own any robots even though robots have been around for 30 years, 50 years even, because to get a robot to do something it just requires a lot of very brutal programming and mechanical engineering and fixtures and hard, up-front costs that then make the system inflexible. 

We're able to throw all of that away and instead provide them with robotic labor as the service, the same way we can get computers to service these days. It's much more flexible and dynamic than would be possible using any other system other than Vicarious provides. We're actually serving multiple customers and our systems are running almost 24, seven doing real work in factories in America. We can do a bunch of different tasks. 

You can see at the website if you want to dig a little bit deeper, but it's everything from packaging, machine tending, kitting, palletizing, depalletizing, sorting. 

There are many different tasks we support and many more coming soon.

And then these are classically things that would be sort of single purpose. There are definitely robotics companies that have done single things really, really well per se, but none that are sort of general purpose across so many different tasks.

For us, the difference between us and many of the robotics companies that exist that are much smaller than we are is the average robotics company builds one product and has to charge a lot of money for it because they just have one product. And even building one robotics product is very difficult and so, you have to build a whole bunch of custom stuff to get it to work. And so, you're not able to advertise the cost of your product over a very large customer base or over a large set of applications. 

Whereas for us, we just have one AI layer we've spent the last 10 years building. And so, because it's so comprehensive, we can create new applications relatively quickly. And we can advertise all of the costs of doing a deployment over a customer who needs 50 or a hundred or 500 robots. And so, that makes the economics much better for us and much better for them.

You know recently I was on an episode of Netflix's explained series by Vox. And the topic was coding. And one interesting direction they took that I didn't expect but it was definitely right was they started identifying that when you talk about traditional robotics approaches, what you're talking about is rooms full of coders writing code that would do one specific purpose within a factory, for instance. Like just picking this up and putting it in this other thing, right, loading and unloading. It's like just one vertical and you can have a team of engineers working on just that one thing. There's this coming revolution where the machines can program themselves. That sort of sounds like what you've already started working on, basically. One offsets of being programmers that are sort of doing these sort of infinitely deep learning projects that only do that one thing, they're really learning systems that learn how to learn.

And you can see some of that in the Science Robotics paper in terms of in that one, you really know this program itself. Like you show it a pair of diagrams in one diagram the apples and oranges are all mixed together and in the second diagram, the apples are on the right and the oranges are on the left. And then you show it maybe two or three examples of that and it figures out, okay, the program that you're trying to communicate to me is sort apples and oranges and on the left and on the right in this way. And then it literally writes its own code. And then it executes that code on the robot. So, that's exactly the direction that we've gone with it and continue to go.

What advice do you have for people who are sort of us, but when we're 18 or 22, just starting out, maybe just learning to code or reading the right things, following the right people, but what does society want from us builders and creators, right? What advice would you share to the 18 or 22-year-old version of yourself based on what you know now?

How much time do you have? I would emphasize how hard it is. I think that a lot of emphasis in the startup world on building short-term, get it to market, flip the company and move onto the next thing kinds of businesses and products for markets that are already over-served. So, I think about things that are primarily consumer apps or in heavily-saturated markets. And then there's very few people who take an interest in more esoteric, but important building blocks in society. 

So, I think about businesses like SpaceX or like Flexport or like Coinbase, who are now very large and successful. But to build them, you had to be kind of weird and very interested in something that most people were too busy building social media apps or building something that was much more short-term or narrowly focused to want to invest time in. 

I guess the advice or encouragement I could give to young people who are thinking about this as a line of work is just to reflect very hard on what is the biggest gift you feel like you can give to your fellow humans and decide with full intention to make that your life's purpose. And know that it's going to be incredibly hard and painful and you'll be met with resistance, but ultimately by being able to give your largest gift to society, you're also giving it to yourself. 

You'll feel the most fulfillment by knowing that you've become who you were supposed to be. I guess that's the advice that I would offer to young people who are thinking about having a career in startups or core technologies. 

Technology is the ultimate lever humanity has ever found on creating a better life for everyone here. And you can be part of that, but I would encourage you to be part of it in a way that leverages your unique gifts and your unique weirdness is the wrong word, but interests and set of backgrounds and passions. There might be something that you're very, very interested in that's really unusual. 

I remember meeting Ben Silbermann, who started Pinterest, and he showed me the demo and it was him and his co-founder or something at the time and he was like, you can collect things, like make websites where you have pictures of all your stuff. And I'm like, why would anyone use this? Or even you, Garry, when you showed me Posterous at the first meeting of our YC class. I'm like, what are you building? And you're like, it's email for blogs. You're like, email can make a blog. How did you get in? [laughs]

And so, I think if you can tap into what you really feel in your bones is the gift that you have to give to all the other humans and then build, build, build to try and make it so. And be prepared for setbacks, but stay true to that gift that you want to give. That's where I would apply my energy.

I feel like even in this interview you've been able to share both of those things. We talked about AGI, it's probably the biggest thing that humanity will see in the next hundred years or ideally much sooner than that and you're trying to bring it about much sooner than that. But at the same time, you're able to basically bring it down to the level of how do I make money? And how do we actually economically make a difference in the lives of your customers, the people who, they're making widgets, they're making X and there's a top line and a bottom line and the technology can actually be applied already in a way that puts dollars and cents in peoples' pockets and it's economically viable and that's what can really grow, like you said earlier. That's really powerful. Thanks for modeling it. It's really, really cool and also rare. What you've been able to do is very, very rare.

You know, as we were talking about it, it just reminded me of how hard it is. 

I think if you go to Vicarious' website, you see a list of all of the most famous people as our investors and all these publications and the highest profile possible academic venues and our incredible team and really polished videos of the robots doing work and it's been 10 years. It's been an incredible and at times wonderful and at times heartbreaking journey to build the company. 

And I think anyone who's trying to do something that is their calling, is their destiny, it's going to have that character to it where on the outside. Like you look at what Elon's been able to do and on the outside you're like, wow, electric cars and tunnels and spaceships, that's just super cool. But you hear him talk about the moments where he had to risk it all or where he was sleeping on the factory floor and all the short sellers were squeezing the life out of the company and it was on the verge of bankruptcy so many times.

I want people who choose to walk this path to choose it with eyes open about that it's going to be hard. And there's going to be moments that you doubt that you should be doing it or that it's going to work out and that's part of the choice to do it. 

I knew when I started Vicarious I want to build AGI. And we pitched it to virtually every investor in and yet the only ones who said yes were the ones who backed me in my previous company, basically, with a couple of exceptions like Dustin Moskovitz and I knew and with eyes open committed myself. 

This may not go anywhere, this may be incredibly hard and I'm comfortable spending the next decade working kind of by myself on this thing that maybe no one else winds up caring about or doesn't go anywhere because it was that important to me. And I would find something that you feel that way about and commit to that because in those darkest moments, that's what will keep you going. 

There's no pot of gold or exit or shiny article in Forbes or something that makes it all worth it. The thing that makes it all worth it is being true to yourself and being true to know that this is what I'm supposed to be doing even if it's impossibly difficult from time to time.